Crowdsourced coordination through online games

Tavakoli, Arash; Nalbandian, Haig; Ayanian, Nora

doi:10.1109/hri.2016.7451839

Cited by 8 publications

(9 citation statements)

References 4 publications

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“…Figure 4 shows the initial state and the final state just after the objective was fulfilled in a representative instance that we ran as part of our pilot 2 investigation [5]. Here the objective of the game was for the team to organize into a rectangle of unified color using both the Overhead and Neighborhood Views.…”

Section: Resultsmentioning

confidence: 99%

Multiplayer Games for Learning Multirobot Coordination Algorithms

Tavakoli¹,

Nalbandian²,

Ayanian³

2016

Preprint

Self Cite

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Humans have an impressive ability to solve complex coordination problems in a fully distributed manner. This ability, if learned as a set of distributed multirobot coordination strategies, can enable programming large groups of robots to collaborate towards complex coordination objectives in a way similar to humans. Such strategies would offer robustness, adaptability, fault-tolerance, and, importantly, distributed decision-making. To that end, we have designed a networked gaming platform to investigate human group behavior, specifically in solving complex collaborative coordinated tasks. Through this platform, we are able to limit the communication, sensing, and actuation capabilities provided to the players. With the aim of learning coordination algorithms for robots in mind, we define these capabilities to mimic those of a simple ground robot.

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Section: Resultsmentioning

confidence: 99%

Multiplayer Games for Learning Multirobot Coordination Algorithms

Tavakoli¹,

Nalbandian²,

Ayanian³

2016

Preprint

Self Cite

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“…To find the corresponding behavioral rules, experimental studies are applied, e.g., for single avoidance tasks among pedestrians [137]. For example, in [138] they observed environmental distributed humans with limited perception to solve a collective coordination task. This was one of the first experiments, before designing formal strategies to learn human-inspired behavior and maybe adapting it to CPS swarms as well.…”

Section: Human Cognitionmentioning

confidence: 99%

Swarm Intelligence and cyber-physical systems: Concepts, challenges and future trends

Schranz

Schmickl

et al. 2021

Swarm and Evolutionary Computation

117

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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“…How do the operators tend to use the swarm to align it with their goals (Walker et al, 2012;Woods & Hollnagel, 2006)? How do operators coordinate goals and actions with other agents of the socio-technical system (Coucke, Heinrich, Cleeremans, & Dorigo, 2020;Ellwart, 2011;Ellwart, Happ, Gurtner, & Rack, 2015;Klein, Woods, Bradshaw, Hoffman, & Feltovich, 2004;Tavakoli, Nalbandian, & Ayanian, 2016;Woods & Hollnagel, 2006)? How does the human operator's mental model about swarm control tend to change over time, and how should the operator be trained in swarm control (Hasbach et al, 2020)?…”

Section: Holistic Perspective On Hsimentioning

confidence: 99%

The design of self-organizing human–swarm intelligence

Hasbach

Bennewitz

2021

Adaptive Behavior

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Human–swarm interaction is a frontier in the realms of swarm robotics and human-factors engineering. However, no holistic theory has been explicitly formulated that can inform how humans and robot swarms should interact through an interface while considering real-world demands, the relative capabilities of the components, as well as the desired joint-system behaviours. In this article, we apply a holistic perspective that we refer to as joint human–swarm loops, that is, a cybernetic system made of human, swarm and interface. We argue that a solution for human–swarm interaction should make the joint human–swarm loop an intelligent system that balances between centralized and decentralized control. The swarm-amplified human is suggested as a possible design that combines perspectives from swarm robotics, human-factors engineering and theoretical neuroscience to produce such a joint human–swarm loop. Essentially, it states that the robot swarm should be integrated into the human’s low-level nervous system function. This requires modelling both the robot swarm and the biological nervous system as self-organizing systems. We discuss multiple design implications that follow from the swarm-amplified human, including a computational experiment that shows how the robot swarm itself can be a self-organizing interface based on minimal computational logic.

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Crowdsourced coordination through online games

Cited by 8 publications

References 4 publications

Multiplayer Games for Learning Multirobot Coordination Algorithms

Multiplayer Games for Learning Multirobot Coordination Algorithms

Swarm Intelligence and cyber-physical systems: Concepts, challenges and future trends

The design of self-organizing human–swarm intelligence

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